In recent years, the field of artificial intelligence and machine learning has experienced a resurgence due to advances in performance of computer hardware, sizes of training sets, theoretical understanding of artificial intelligence, and other advances. This resurgence has enabled many advances in other technical fields, including recognition or other prediction systems. Existing machine learning systems can classify data, such as x-rays images, magnetic resonance images, volumetric scans (e.g., CT scans), or other data for diagnostic purposes. Such machine learning systems generally rely on the presentation of known anomalies through labels. For example, the classification of retinal pathologies depends on presenting the learning system with millions of images corresponding to known pathologies and helping the learning system classify each of the images as corresponding to one of the known pathologies. When presented with an image corresponding to an unknown pathology, however, the learning system would classify the image as corresponding to one of the known pathologies even where the image does not match any of the known pathologies. These and other drawbacks exist.